1. Field of Art
The disclosure generally relates to video processing, and more particularly, to adaptively selecting quantization parameters within a video processing system to more efficiently compress video images.
2. Description of the Related Art
Video compression is critical for many multimedia applications available today. For applications such as DVD, digital television broadcasting, satellite television, Internet video streaming, video conferencing, video security, and digital camcorders, limited transmission bandwidth or storage capacity stresses the demand for higher compression ratios. A key component in high-compression video coding system is an operational control of an encoder through predictions, transformations and quantization. To efficiently compress video signals, each transform coefficient of video signal from the transformation process, such as Discrete Cosine Transform (DCT), is further quantized by a quantization parameter that is commonly defined by a quantizer step size. The quantization parameter (QP) regulates how much spatial detail is saved. When QP is very small, almost all that detail is retained.
As QP is increased, some of that detail is aggregated, which drops the bit rate required. However, this process increases distortion and causes some loss of quality. As a goal of video compression system to achieve the best fidelity (or the lowest distortion) given the capacity of transmission channel, subject to the coding rate constraint, an appropriately selected quantization parameter can have an enormous impact on achieving the maximum perceptual quality of the reconstructed video picture for a predetermined target bit rate.
The task of designing a video coding system that produces undetectable errors in the reconstructed video images with minimum transmitted bits is difficult. Selection of QP in conventional video coding systems is statistically based, and often further optimized to find the best quantization step size for each image region to be coded in a rate-distortion sense. However, such a statistical model and optimization is often at an expense of a large amount of memory access and computational complexity.
Another problem of the conventional QP selection is that widely varying content and motion of video signals are often not taken into consideration during QP selection. The perceived distortion in visual content is a very difficult quantity to measure, as the characteristics of human visual system are complex and not well understood. This problem is aggravated in video coding, because addition of the temporal domain relative to still images coding complicates the issue. For example, human viewers are more sensitive to reconstruction errors related to low spatial frequencies, such as slow linear changes in intensity or color, than those related to high frequencies. Furthermore, the visibility of distortion depends to a great extent on video image content. In particular, distortions are often much more disturbing in relatively smooth area of an image than in texture regions with a lot of activities.
Accordingly, there is a need for a system and method that adaptively selects quantization parameter for each image region to be coded so that the perceptual quality of the reconstructed video images is efficiently improved.